A Generalization of Gurland's Theorem,with Applications to Economic Behavior under Uncertainty

We illustrate the use of the Mathematica software system (programming language) for performing Bayesian calculations of the sort encountered in introductory presentations of Bayesian statistics. In particular, we show the ease with which one can do numerical computation, graphics, and symbolic computation to analyze one- and two-dimensional probability density functions. Our illustrations involve a time series problem using oxygen uptake data taken from a burn patient.